Abstract
Textual patterns (e.g., Country’s president Person) are specified and/or generated for extracting factual information from unstructured data. Pattern-based information extraction methods have been recognized for their efficiency and transferability. However, not every pattern is reliable: A major challenge is to derive the most complete and accurate facts from diverse and sometimes conflicting extractions. In this work, we propose a probabilistic graphical model which formulates fact extraction in a generative process. It automatically infers true facts and pattern reliability without any supervision. It has two novel designs specially for temporal facts: (1) it models pattern reliability on two types of time signals, including temporal tag in text and text generation time; (2) it models commonsense constraints as observable variables. Experimental results demonstrate that our model significantly outperforms existing methods on extracting true temporal facts from news data.- Anthology ID:
- 2020.fever-1.3
- Volume:
- Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER)
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Christos Christodoulopoulos, James Thorne, Andreas Vlachos, Oana Cocarascu, Arpit Mittal
- Venue:
- FEVER
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 18–25
- Language:
- URL:
- https://aclanthology.org/2020.fever-1.3
- DOI:
- 10.18653/v1/2020.fever-1.3
- Cite (ACL):
- Yang Zhou, Tong Zhao, and Meng Jiang. 2020. A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction. In Proceedings of the Third Workshop on Fact Extraction and VERification (FEVER), pages 18–25, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Probabilistic Model with Commonsense Constraints for Pattern-based Temporal Fact Extraction (Zhou et al., FEVER 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.fever-1.3.pdf